Merge branch 'main' into torch-prof

README.md

@@ -150,8 +150,8 @@ See [Segmentation Docs](https://docs.ultralytics.com/tasks/segment/) for usage e
 | [YOLO11l-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l-seg.pt) | 640                   | 53.4                 | 42.9                  | 344.2 ± 3.2                    | 7.8 ± 0.2                           | 27.6               | 142.2             |
 | [YOLO11x-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x-seg.pt) | 640                   | 54.7                 | 43.8                  | 664.5 ± 3.2                    | 15.8 ± 0.7                          | 62.1               | 319.0             |
 
-- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](https://cocodataset.org/) dataset. <br>Reproduce by `yolo val segment data=coco-seg.yaml device=0`
-- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val segment data=coco-seg.yaml batch=1 device=0|cpu`
+- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](https://cocodataset.org/) dataset. <br>Reproduce by `yolo val segment data=coco.yaml device=0`
+- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val segment data=coco.yaml batch=1 device=0|cpu`
 
 </details>
 

README.zh-CN.md

@@ -150,8 +150,8 @@ YOLO11 [检测](https://docs.ultralytics.com/tasks/detect/)、[分割](https://d
 | [YOLO11l-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l-seg.pt) | 640                 | 53.4                 | 42.9                  | 344.2 ± 3.2                   | 7.8 ± 0.2                          | 27.6             | 142.2             |
 | [YOLO11x-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x-seg.pt) | 640                 | 54.7                 | 43.8                  | 664.5 ± 3.2                   | 15.8 ± 0.7                         | 62.1             | 319.0             |
 
-- **mAP<sup>val</sup>** 值针对单模型单尺度在 [COCO val2017](https://cocodataset.org/) 数据集上进行。 <br>复制命令 `yolo val segment data=coco-seg.yaml device=0`
-- **速度**在使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例的 COCO 验证图像上平均。 <br>复制命令 `yolo val segment data=coco-seg.yaml batch=1 device=0|cpu`
+- **mAP<sup>val</sup>** 值针对单模型单尺度在 [COCO val2017](https://cocodataset.org/) 数据集上进行。 <br>复制命令 `yolo val segment data=coco.yaml device=0`
+- **速度**在使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例的 COCO 验证图像上平均。 <br>复制命令 `yolo val segment data=coco.yaml batch=1 device=0|cpu`
 
 </details>
 

docs/en/datasets/pose/hand-keypoints.md

@@ -10,6 +10,17 @@ keywords: Hand KeyPoints, pose estimation, dataset, keypoints, MediaPipe, YOLO,
 
 The hand-keypoints dataset contains 26,768 images of hands annotated with keypoints, making it suitable for training models like Ultralytics YOLO for pose estimation tasks. The annotations were generated using the Google MediaPipe library, ensuring high [accuracy](https://www.ultralytics.com/glossary/accuracy) and consistency, and the dataset is compatible [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics) formats.
 
+<p align="center">
+  <br>
+  <iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/fd6u1TW_AGY"
+    title="YouTube video player" frameborder="0"
+    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
+    allowfullscreen>
+  </iframe>
+  <br>
+  <strong>Watch:</strong> Hand Keypoints Estimation with Ultralytics YOLO11 | Human Hand Pose Estimation Tutorial
+</p>
+
 ## Hand Landmarks
 
 ![Hand Landmarks](https://github.com/ultralytics/docs/releases/download/0/hand_landmarks.jpg)

docs/en/datasets/segment/coco.md

@@ -56,14 +56,14 @@ To train a YOLO11n-seg model on the COCO-Seg dataset for 100 [epochs](https://ww
         model = YOLO("yolo11n-seg.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
-        results = model.train(data="coco-seg.yaml", epochs=100, imgsz=640)
+        results = model.train(data="coco.yaml", epochs=100, imgsz=640)
         ```
 
     === "CLI"
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo segment train data=coco-seg.yaml model=yolo11n-seg.pt epochs=100 imgsz=640
+        yolo segment train data=coco.yaml model=yolo11n-seg.pt epochs=100 imgsz=640
         ```
 
 ## Sample Images and Annotations
@@ -118,14 +118,14 @@ To train a YOLO11n-seg model on the COCO-Seg dataset for 100 epochs with an imag
         model = YOLO("yolo11n-seg.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
-        results = model.train(data="coco-seg.yaml", epochs=100, imgsz=640)
+        results = model.train(data="coco.yaml", epochs=100, imgsz=640)
         ```
 
     === "CLI"
 
         ```bash
         # Start training from a pretrained *.pt model
-        yolo segment train data=coco-seg.yaml model=yolo11n-seg.pt epochs=100 imgsz=640
+        yolo segment train data=coco.yaml model=yolo11n-seg.pt epochs=100 imgsz=640
         ```
 
 ### What are the key features of the COCO-Seg dataset?

docs/en/tasks/segment.md

@@ -36,8 +36,8 @@ YOLO11 pretrained Segment models are shown here. Detect, Segment and Pose models
 
 {% include "macros/yolo-seg-perf.md" %}
 
-- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](https://cocodataset.org/) dataset. <br>Reproduce by `yolo val segment data=coco-seg.yaml device=0`
-- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val segment data=coco-seg.yaml batch=1 device=0|cpu`
+- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](https://cocodataset.org/) dataset. <br>Reproduce by `yolo val segment data=coco.yaml device=0`
+- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val segment data=coco.yaml batch=1 device=0|cpu`
 
 ## Train
 

docs/en/usage/simple-utilities.md

@@ -458,6 +458,17 @@ image_with_obb = ann.result()
 
 #### Bounding Boxes Circle Annotation [Circle Label](https://docs.ultralytics.com/reference/utils/plotting/#ultralytics.utils.plotting.Annotator.circle_label)
 
+<p align="center">
+  <br>
+  <iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/c-S5M36XWmg"
+    title="YouTube video player" frameborder="0"
+    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
+    allowfullscreen>
+  </iframe>
+  <br>
+  <strong>Watch:</strong> In-Depth Guide to Text & Circle Annotations with Python Live Demos | Ultralytics Annotations 🚀
+</p>
+
 ```python
 import cv2
 

ultralytics/data/augment.py

@@ -1591,7 +1591,7 @@ class LetterBox:
             labels["ratio_pad"] = (labels["ratio_pad"], (left, top))  # for evaluation
 
         if len(labels):
-            labels = self._update_labels(labels, ratio, dw, dh)
+            labels = self._update_labels(labels, ratio, left, top)
             labels["img"] = img
             labels["resized_shape"] = new_shape
             return labels